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Load Libraries


### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)
ud_model <- udpipe_download_model(language = "english")
library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
library(quanteda)
library(lattice)
library(latticeExtra)
library(plotly)
library(pdp)
library(patchwork)
library(lubridate)

Load the FW functions


### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#
fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}

Compare Associated Press 1994-2010: Before and After “extremist”

Load and clean the data


######################################

### Text Cleaning Function

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

######################################

Calculate FW.

[1] 0.9900516

##################################################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)
Error: vector memory exhausted (limit reached?)

Get and show the top words per group by zeta.

##################################################################

fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
cols <- rev(colnames(fwkeys.extrem))
fwkeys.extrem <- fwkeys.extrem[,cols]

##################################################################

kable(fwkeys.extrem)
Context.before Context.after
muslim group
jewish organ
rightw movement
islamic respons
member element
rrb hutu
suspect network
belong ideolog
ban militia
lrb oppos
alqaidalink view
small activ
rabin bomb
sunni sayyaf
assassin threaten
ap parti
alleg abu
outlaw attack
yitzhak alqaida
help govern

##################################################################

Plot: Comparing Words Before (in Blue), and After (in Red)

p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"),
                                 title = 'Comparison of Terms Before and After "Extremist"')
p.fw.extrem

Calculate Parts of speech by before and after

##################################################################

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)


##################################################################
##################################################################

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

##################################################################

Bar Charts for Parts of Speech

Cooccurences

Cooccurences (part 2)


Corrs(x_odd.before)
Corrs(x_even.after)
NA

Extrem(ist) Fightin’ Words Over Time

library(tweenr)

TransitionTime2 <- ggproto(
  "TransitionTime2",
  TransitionTime,
  expand_panel = function (self, data, type, id, match, ease, enter, exit, params, 
                           layer_index) {
    row_time <- self$get_row_vars(data)
    if (is.null(row_time)) 
      return(data)
    data$group <- paste0(row_time$before, row_time$after)
    time <- as.integer(row_time$time)
    states <- split(data, time)
    times <- as.integer(names(states))
    nframes <- diff(times)
    nframes[1] <- nframes[1] + 1
    if (times[1] <= 1) {
      all_frames <- states[[1]]
      states <- states[-1]
    }
    else {
      all_frames <- data[0, , drop = FALSE]
      nframes <- c(times[1] - 1, nframes)
    }
    if (times[length(times)] < params$nframes) {
      states <- c(states, list(data[0, , drop = FALSE]))
      nframes <- c(nframes, params$nframes - times[length(times)])
    }
    for (i in seq_along(states)) {
      all_frames <- switch(type, point = tween_state(all_frames, 
                                                     states[[i]], ease, nframes[i], 
                                                     !!id, enter, exit), 
                           path = transform_path(all_frames, 
                                                 states[[i]], ease, nframes[i], 
                                                 !!id, enter, exit, match), 
                           polygon = transform_polygon(all_frames, 
                                                       states[[i]], ease, nframes[i], 
                                                       !!id, enter, exit, match), 
                           sf = transform_sf(all_frames, 
                                             states[[i]], ease, nframes[i], 
                                             !!id, enter, exit), 
                           stop(type, 
                                " layers not currently supported by transition_time", 
                                call. = FALSE))
    }
    true_frame <- seq(times[1], times[length(times)])
    all_frames <- all_frames[
      all_frames$.frame %in% 
        # which(true_frame > 0 & true_frame <= params$nframes),
        true_frame[which(true_frame > 0 & true_frame <= params$nframes)], # tweak line A
      , 
      drop = FALSE]
    # all_frames$.frame <- all_frames$.frame - min(all_frames$.frame) + 1 # remove line B
    all_frames$group <- paste0(all_frames$group, "<", all_frames$.frame, ">")
    all_frames$.frame <- NULL
    all_frames
  })

transition_time2 <- function (time, range = NULL) {
  time_quo <- enquo(time)
  gganimate:::require_quo(time_quo, "time")
  ggproto(NULL, TransitionTime2, 
          params = list(time_quo = time_quo, range = range))
}

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home

---
title: "Extrem(ist) Fightin' Words"
author: "Breanna E. Green"
subtitle: 'Associated Press 1994-2010: Keyword:"Extremist"'
output:
  html_document:
    toc: yes
    df_print: paged
  html_notebook:
    code_folding: show
    df_print: paged
    highlight: tango
    theme: united
    toc: yes
---

[home](https://bregreen.github.io/)

## Load Libraries

```{r load libraries, results='hide'}

### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)
ud_model <- udpipe_download_model(language = "english")

library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
library(quanteda)
library(lattice)
library(latticeExtra)
library(plotly)
library(pdp)
library(patchwork)
library(lubridate)

```

## Load the FW functions

```{r load_fw_functions}

### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#
fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}
```


## Compare Associated Press 1994-2010: Before and After "extremist"

*Load and clean the data*

  * to string & lower text
  * pivot to long format
  * apply text_cleaner to one column "context.text"

```{r}

######################################

### Text Cleaning Function

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

######################################

```

```{r clean_text, echo=FALSE, results= FALSE}

extrem_AP.dfm <- read.delim("extremist_9410_AP.txt", header = TRUE, sep = "\t")
extrem_AP.dfm$pubdate = substr(extrem_AP.dfm$Text.ID,9,16)
extrem_AP.dfm$pubdate <- as.POSIXct(extrem_AP.dfm$pubdate, format = "%Y%m%d")
extrem_AP.dfm$pubdate <- as.Date(extrem_AP.dfm$pubdate, format="%Y-%m-%d")
extrem_Ap.dfm$pubyear <- year(extrem_AP.dfm$pubdate)

# extrem_NYT.dfm$Context.before = lapply(extrem_NYT.dfm$Context.before, toString)
# extrem_NYT.dfm$Context.before = lapply(extrem_NYT.dfm$Context.before, tolower)
# 
# extrem_NYT.dfm$Context.after = lapply(extrem_NYT.dfm$Context.after, toString)
# extrem_NYT.dfm$Context.after = lapply(extrem_NYT.dfm$Context.after, tolower)

extrem_AP.dfm <- extrem_AP.dfm %>% distinct(Context.before, .keep_all = TRUE)

extrem_AP.dfm.long <- pivot_longer(extrem_AP.dfm, cols=c(Context.before, Context.after), names_to = "Context", values_to = "context.text")

extrem_AP.dfm.long$Context <- as.factor(extrem_AP.dfm.long$Context)

extremecorpus <-text_cleaner(extrem_AP.dfm.long$context.text)

```


Calculate FW.

```{r, message=FALSE, echo=FALSE}
#############################################
e <- dfm(extremecorpus$content)
message(dim(e))

#############################################

e <- dfm_select(e, pattern = stopwords("english"), selection = "remove")
e <- dfm_select(e, min_nchar = 2)
e <- dfm_trim(e, min_termfreq = 100, min_docfreq = .05, verbose=TRUE)

message(dim(e))
message(sparsity(e))
sparsity(e)

#############################################

extrem_dtm <- convert(e, to='data.frame')
extrem_dtm <- extrem_dtm[-c(1)]
w <- which( sapply(extrem_dtm, class ) == 'character' )

```

```{r, message=FALSE, echo=TRUE}

##################################################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_AP.dfm.long$Context)

##################################################################

message(rm(extrem_dtm))
message(rm(e))

```


Get and show the top words per group by zeta.

```{r echo=TRUE}
##################################################################

fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
cols <- rev(colnames(fwkeys.extrem))
fwkeys.extrem <- fwkeys.extrem[,cols]

##################################################################

kable(fwkeys.extrem)

##################################################################

```

*Plot: Comparing Words Before (in Blue), and After (in Red)*

```{r, fig.height=5, fig.width=4}
p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"),
                                 title = 'Comparison of Terms Before and After "Extremist"')
p.fw.extrem

```

## Calculate Parts of speech by before and after

```{r, results='hide'}
##################################################################

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_AP.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)


##################################################################


```

```{r, results='hide'}
##################################################################

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

##################################################################

```

```{r barchartfuncts, echo=FALSE}

## UNIVERSAL PoS
UPOS_barchart <- function(df1, df2){
  
  stats1 <- txt_freq(df1$upos)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- txt_freq(df2$upos)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = stats1, col = "cadetblue", 
        main = "UPOS (Universal Parts of Speech)\n frequency of occurrence: BEFORE vs AFTER", 
         xlab = "Freq"), 
    barchart(key ~ freq, data = stats2, col =  'skyblue',
         xlab = "Freq"))

}


## NOUNS
NOUNS_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("NOUN")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("NOUN")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring nouns: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
            xlab = "Freq"))
}

## ADJECTIVES
ADJ_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("ADJ")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("ADJ")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring adjectives: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
         xlab = "Freq"))
}

## Using RAKE to find keywords
RAKE_KW_barchart <- function(df1,df2){
  
  stats1 <- keywords_rake(x = df1, term = "lemma", group = "doc_id", 
                         relevant = df1$upos %in% c("NOUN", "ADJ"))
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  stats2 <- keywords_rake(x = df2, term = "lemma", group = "doc_id", 
                         relevant = df2$upos %in% c("NOUN", "ADJ"))
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  
  c(barchart(key ~ rake, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by RAKE: BEFORE vs AFTER", 
           xlab = "Rake"),
    barchart(key ~ rake, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "Rake"))
}

## Using Pointwise Mutual Information Collocations
PWI_barchart <- function(df1, df2){
  
  df1$word <- tolower(df1$token)
  stats1 <- keywords_collocation(x = df1, term = "word", group = "doc_id")
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$word <- tolower(df2$token)
  stats2 <- keywords_collocation(x = df2, term = "word", group = "doc_id")
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ pmi, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by PMI Collocation: BEFORE vs AFTER", 
           xlab = "PMI (Pointwise Mutual Information)"),
      barchart(key ~ pmi, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "PMI (Pointwise Mutual Information)"))
}

## Using a sequence of POS tags (noun phrases / verb phrases)
POS_barchart <- function(df1, df2){
  
  df1$phrase_tag <- as_phrasemachine(df1$upos, type = "upos")
  stats1 <- keywords_phrases(x = df1$phrase_tag, term = tolower(df1$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats1 <- subset(stats1, ngram > 1 & freq > 3)
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$phrase_tag <- as_phrasemachine(df2$upos, type = "upos")
  stats2 <- keywords_phrases(x = df2$phrase_tag, term = tolower(df2$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats2 <- subset(stats2, ngram > 1 & freq > 3)
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Keywords - simple noun phrases: BEFORE vs AFTER", xlab = "Frequency"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
               xlab = "Frequency"))
}
```


## Bar Charts for Parts of Speech 

```{r POSbarcharts, echo=FALSE, fig.width=8}
##################################################################

UPOS_barchart(x_odd.before, x_even.after)

##################################################################

NOUNS_barchart(x_odd.before, x_even.after)

##################################################################

ADJ_barchart(x_odd.before, x_even.after)

##################################################################

RAKE_KW_barchart(x_odd.before, x_even.after)

##################################################################

PWI_barchart(x_odd.before, x_even.after)

##################################################################

POS_barchart(x_odd.before, x_even.after)

##################################################################
```


## Cooccurences

```{r, echo=FALSE, fig.width=6}

CO_OC_noun_adj_same_sent.before <- function(df1){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df1, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective")
  
}

CO_OC_noun_adj_same_sent.after <- function(df2){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df2, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective")
  
}


CO_OC_noun_adj_same_sent.before(x_odd.before)
CO_OC_noun_adj_same_sent.after(x_even.after)


##########################################

CO_OC_noun_adj_following.before <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: BEFORE", subtitle = "Nouns & Adjective")
}

CO_OC_noun_adj_following.after <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "skyblue") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: AFTER", subtitle = "Nouns & Adjective")
}


CO_OC_noun_adj_following.before(x_odd.before)
CO_OC_noun_adj_following.after(x_even.after)

```



## Cooccurences (part 2)

```{r, echo=FALSE}

Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  y[1:25,]
}

```

```{r}

Corrs(x_odd.before)
Corrs(x_even.after)

```


# Extrem(ist) Fightin' Words Over Time
```{r}
library(tweenr)

TransitionTime2 <- ggproto(
  "TransitionTime2",
  TransitionTime,
  expand_panel = function (self, data, type, id, match, ease, enter, exit, params, 
                           layer_index) {
    row_time <- self$get_row_vars(data)
    if (is.null(row_time)) 
      return(data)
    data$group <- paste0(row_time$before, row_time$after)
    time <- as.integer(row_time$time)
    states <- split(data, time)
    times <- as.integer(names(states))
    nframes <- diff(times)
    nframes[1] <- nframes[1] + 1
    if (times[1] <= 1) {
      all_frames <- states[[1]]
      states <- states[-1]
    }
    else {
      all_frames <- data[0, , drop = FALSE]
      nframes <- c(times[1] - 1, nframes)
    }
    if (times[length(times)] < params$nframes) {
      states <- c(states, list(data[0, , drop = FALSE]))
      nframes <- c(nframes, params$nframes - times[length(times)])
    }
    for (i in seq_along(states)) {
      all_frames <- switch(type, point = tween_state(all_frames, 
                                                     states[[i]], ease, nframes[i], 
                                                     !!id, enter, exit), 
                           path = transform_path(all_frames, 
                                                 states[[i]], ease, nframes[i], 
                                                 !!id, enter, exit, match), 
                           polygon = transform_polygon(all_frames, 
                                                       states[[i]], ease, nframes[i], 
                                                       !!id, enter, exit, match), 
                           sf = transform_sf(all_frames, 
                                             states[[i]], ease, nframes[i], 
                                             !!id, enter, exit), 
                           stop(type, 
                                " layers not currently supported by transition_time", 
                                call. = FALSE))
    }
    true_frame <- seq(times[1], times[length(times)])
    all_frames <- all_frames[
      all_frames$.frame %in% 
        # which(true_frame > 0 & true_frame <= params$nframes),
        true_frame[which(true_frame > 0 & true_frame <= params$nframes)], # tweak line A
      , 
      drop = FALSE]
    # all_frames$.frame <- all_frames$.frame - min(all_frames$.frame) + 1 # remove line B
    all_frames$group <- paste0(all_frames$group, "<", all_frames$.frame, ">")
    all_frames$.frame <- NULL
    all_frames
  })

transition_time2 <- function (time, range = NULL) {
  time_quo <- enquo(time)
  gganimate:::require_quo(time_quo, "time")
  ggproto(NULL, TransitionTime2, 
          params = list(time_quo = time_quo, range = range))
}
```

```{r extra_loads_odd_before, echo=FALSE}
library(ggraph)
library(ggforce)
library(gganimate)
library(ggplot2)
library(igraph)
library(animation)
library(lubridate)


results<-merge(x=x,y=extrem_AP.dfm.long, by.x='doc_id',  by.y = 0  ,all.x=TRUE)
results_odd.before <- results[results$doc_id %% 2 == 1,]
results_even.after <-results[results$doc_id %% 2 == 0, ]

cooc_year<- data.frame()

for(y in unique(results_odd.before$pubyear)){
  # print(y)
  cooc <- cooccurrence(x = subset(filter(results_odd.before, pubyear == y), upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))
  cooc$year <- y
  cooc<- head(cooc, 14)
  cooc_year <- rbind(cooc_year, cooc)
}

wordnetwork <- cooc_year

ggraph(wordnetwork, layout = "fr") +
  geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
  geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
  theme_graph(base_family = "Arial Narrow") +
  theme(legend.position = "none") +
  # facet_wrap_paginate(~year)+
  scale_alpha_identity()+
  labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective {frame_time}")+
  transition_time2(year)
  ease_aes('linear')

# plot(p)
# p

# last_animation()

```
```{r extra_loads_even_after, echo=FALSE}

cooc_year<- data.frame()

for(y in unique(results_even.after$pubyear)){
  # print(y)
  cooc <- cooccurrence(x = subset(filter(results_even.after, pubyear == y), upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))
  cooc$year <- y
  cooc<- head(cooc, 14)
  cooc_year <- rbind(cooc_year, cooc)
}


wordnetwork <- cooc_year

ggraph(wordnetwork, layout = "fr") +
  geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
  geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
  theme_graph(base_family = "Arial Narrow") +
  theme(legend.position = "none") +
  # facet_wrap_paginate(~year)+
  scale_alpha_identity()+
  labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective {frame_time}")+
  transition_time2(year)
  ease_aes('linear')

# plot(p2)
# p2

# last_animation()

```

```{r cooc2_before, echo=FALSE}

cooc2_year<- data.frame()

for(y in unique(results_odd.before$pubyear)){
  # print(y)
  a<- filter(results_odd.before, pubyear == y)
  
  cooc <- cooccurrence(a$lemma, relevant = a$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  cooc$year <- y
  cooc<- head(cooc, 25)
  cooc2_year <- rbind(cooc2_year, cooc)
}


wordnetwork <- cooc2_year

ggraph(wordnetwork, layout = "fr") +
  geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
  geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
  theme_graph(base_family = "Arial Narrow") +
  theme(legend.position = "none") +
  # facet_wrap_paginate(~year)+
  scale_alpha_identity()+
  labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective {frame_time}")+
  transition_time2(year)
  ease_aes('linear')

# plot(p3)
# p3

# last_animation()

```
```{r cooc2_after, echo=FALSE}

cooc2_year<- data.frame()

for(y in unique(results_even.after$pubyear)){
  # print(y)
  a<- filter(results_even.after, pubyear == y)
  
  cooc <- cooccurrence(a$lemma, relevant = a$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  cooc$year <- y
  cooc<- head(cooc, 12)
  cooc2_year <- rbind(cooc2_year, cooc)
}


wordnetwork <- cooc2_year

ggraph(wordnetwork, layout = "fr") +
  geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
  geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
  theme_graph(base_family = "Arial Narrow") +
  theme(legend.position = "none") +
  # facet_wrap_paginate(~year)+
  scale_alpha_identity()+
  labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective {frame_time}")+
  transition_time2(year)
  ease_aes('linear')

# plot(p4)
# p4
# last_animation()

```




```{r final, echo=FALSE}

rm(list=ls())

```








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